Architecture & Roadmap

Partner with Enterprise Architects to define and evolve the trade surveillance technical roadmap, ensuring alignment with firm-wide architecture standards, data strategy, and regulatory expectations.

Own end-to-end architecture for surveillance scenario development, alert generation, case management integration, and downstream investigator tooling.

Define reference architectures, design patterns, and engineering guardrails for the surveillance engineering organization.

Scenario & Alert Engineering

Lead the design and implementation of trade surveillance scenarios and alerts across asset classes (equities, fixed income, FX, derivatives) covering market abuse typologies such as spoofing, layering, wash trades, front-running, insider trading, and cross-product manipulation.

Drive development of scenarios and analytics in both Q/KDB (current state) and Python/PySpark (target state).

Establish reusable frameworks for scenario authoring, parameter tuning, backtesting, threshold calibration, and false-positive reduction.

Platform Modernization & Cloud Migration

Lead migration of the trade surveillance platform to the new cloud infrastructure (AWS / Azure / GCP), including compute, storage, streaming, and orchestration layers.

Architect and oversee the migration of scenarios, libraries, and frameworks from Q/KDB to Python/PySpark on distributed compute (Spark, Databricks, EMR, or equivalent), ensuring functional parity, performance, and auditability.

Design for scale — alert generation across billions of order and trade events per day — with focus on throughput, latency, cost optimization, and resilience.

Data & Engineering Excellence

Define data models and ingestion patterns for orders, executions, market data, reference data, communications, and news/social feeds.

Champion engineering best practices: CI/CD, IaC, automated testing of surveillance logic, observability, lineage, and reproducibility of alerts (critical for regulatory defensibility).

Collaborate with Data Engineering, DevOps, and InfoSec on secure-by-design implementations.

Innovation & GenAI

Identify and prototype applications of Generative AI and ML in surveillance — narrative generation for alerts, investigator copilots, anomaly detection, communications surveillance (NLP), and intelligent triage.

Evaluate vendor and open-source capabilities and guide build-vs-buy decisions.

Leadership & Stakeholder Management

Mentor senior engineers and tech leads; conduct design reviews and uphold architectural quality.

Engage with Compliance, Front Office Supervision, Internal Audit, and Regulators on technical aspects of the surveillance program.

12+ years of technology experience, with significant time spent architecting large-scale data or surveillance/risk platforms in financial services (investment bank, exchange, broker-dealer, or asset manager).

Strong hands-on expertise in Q/KDB+ — schema design, query optimization, real-time and historical analytics on tick data.

Deep proficiency in Python and PySpark, including distributed processing patterns, performance tuning, and production-grade engineering.

Working knowledge of Java/JVM ecosystem (Spring, Kafka clients, JVM tuning) — sufficient to architect cross-stack integrations.

Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and modern data platforms (Databricks, Snowflake, EMR, or equivalent).

Strong grasp of streaming and event-driven architectures (Kafka, Flink, Kinesis, or similar).

Solid understanding of trade lifecycle, market microstructure, and at least one regulatory regime (MAR, Dodd-Frank, MiFID II, SEBI, FINRA rules).

Proven ability to operate at the intersection of engineering and architecture — designing on the whiteboard and coding when needed.

Key Competencies

Architectural thinking balanced with engineering pragmatism.

Strong written and verbal communication; able to translate regulatory and business intent into technical design.

Comfortable navigating ambiguity in a high-stakes, regulator-facing environment.

Influences without authority — credible with senior engineers, enterprise architects, and compliance leadership alike.

Prior experience migrating Q/KDB workloads to Python/Spark-based stacks.

Exposure to commercial surveillance platforms (Nasdaq SMARTS, Eventus Validus, Scila, Actimize, b-next) — either implementing, extending, or replacing them.

Experience with Generative AI / LLM frameworks (LangChain, LlamaIndex, vector databases, RAG patterns) and ML libraries (scikit-learn, PyTorch).

Familiarity with communications surveillance and NLP techniques.

Experience with containerization (Docker, Kubernetes) and IaC (Terraform).

Background in quantitative analytics or low-latency systems.